A self-training algorithm based on the two-stage data editing method with mass-based

doi:10.1016/j.neunet.2023.09.046

	A self-training algorithm based on the two-stage data editing method with mass-based
	Wang, Jikui1 ; Wu, Yiwen1 ; Li, Shaobo2 ; Nie, Feiping3,4
	2023-11
发表期刊	NEURAL NETWORKS
卷号	168 页码:431-449
摘要	A self-training algorithm is a classical semi-supervised learning algorithm that uses a small number of labeled samples and a large number of unlabeled samples to train a classifier. However, the existing self training algorithms consider only the geometric distance between data while ignoring the data distribution when calculating the similarity between samples. In addition, misclassified samples can severely affect the performance of a self-training algorithm. To address the above two problems, this paper proposes a self training algorithm based on data editing with mass-based dissimilarity (STDEMB). First, the mass matrix with the mass-based dissimilarity is obtained, and then the mass-based local density of each sample is determined based on its k nearest neighbors. Inspired by density peak clustering (DPC), this study designs a prototype tree based on the prototype concept. In addition, an efficient two-stage data editing algorithm is developed to edit misclassified samples and efficiently select high-confidence samples during the self-training process. The proposed STDEMB algorithm is verified by experiments using accuracy and F-score as evaluation metrics. The experimental results on 18 benchmark datasets demonstrate the effectiveness of the proposed STDEMB algorithm.
关键词	Self-training algorithm Mass-based dissimilarity Data editing Relative node set
DOI	10.1016/j.neunet.2023.09.046
收录类别	SCIE ; EI
ISSN	0893-6080
语种	英语
WOS研究方向	Computer Science ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号	WOS:001089161100001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
EI入藏号	20234314960252
EI主题词	Learning algorithms
EI分类号	723.4.2 Machine Learning ; 921.5 Optimization Techniques
原始文献类型	Article
EISSN	1879-2782
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/35357
专题	信息工程与人工智能学院工商管理学院
通讯作者	Wang, Jikui
作者单位	1.Lanzhou Univ Finance & Econ, Sch Informat Engn & Artif Intelligence, Lanzhou 730020, Gansu, Peoples R China; 2.Guizhou Univ, State Key Lab Publ Big Data, Guiyang 550025, Guizhou, Peoples R China; 3.Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shanxi, Peoples R China; 4.Northwestern Polytech Univ, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710072, Shanxi, Peoples R China
第一作者单位	兰州财经大学
通讯作者单位	兰州财经大学
推荐引用方式 GB/T 7714	Wang, Jikui,Wu, Yiwen,Li, Shaobo,et al. A self-training algorithm based on the two-stage data editing method with mass-based[J]. NEURAL NETWORKS,2023,168:431-449.
APA	Wang, Jikui,Wu, Yiwen,Li, Shaobo,&Nie, Feiping.(2023).A self-training algorithm based on the two-stage data editing method with mass-based.NEURAL NETWORKS,168,431-449.
MLA	Wang, Jikui,et al."A self-training algorithm based on the two-stage data editing method with mass-based".NEURAL NETWORKS 168(2023):431-449.